scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

scholarly journals Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands

2021 ◽  
Author(s):  
Nicolai T. Borgen ◽  
Andreas Haupt ◽  
Øyvind N. Wiborg
Keyword(s):  
Quantile Regression ◽  
Fixed Effects ◽  
Regression Models ◽  
Treatment Effects ◽  
The Other ◽  
High Dimensional ◽  
Fast Estimation ◽  
Quantile Treatment Effects

Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

scholarly journals Causal Random Forests Model Using Instrumental Variable Quantile Regression

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 49 ◽  
Author(s):  
Jau-er Chen ◽  
Chen-Wei Hsiang
Keyword(s):  
Quantile Regression ◽  
Training Program ◽  
Random Forests ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Financial Assets ◽  
Distributional Effect ◽  
Instrumental Variable Quantile Regression ◽  
Quantile Treatment Effect ◽  
Random Forests Model

We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program.

scholarly journals What Quantile Regression Does and Doesn't Do

2018 ◽  
Author(s):  
Sebastian Ernst Wenz
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Models ◽  
Simulated Data ◽  
Quantile Function ◽  
Linear Regression Models ◽  
Conditional Quantile ◽  
Correct Understanding ◽  
Mean Function ◽  
Conditional Quantile Function

Petscher and Logan (2014)’s description of quantile regression might mislead readers to believe it would estimate the relation between an outcome, y, and one or more predictors, x, at different quantiles of the unconditional distribution of y. However, quantile regression models the conditional quantile function of y given x just as linear regression models the conditional mean function. This article’s contribution is twofold: First, it discusses potential consequences of methodological misconceptions and formulations of Petscher and Logan (2014)’s presentation by contrasting features of quantile regression and linear regression. Secondly, it reinforces the importance of correct understanding of quantile regression in empirical research by illustrating similarities and differences of various quantile regression estimators and linear regression using simulated data.

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